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OCR for page 94
Land-Use Change After Deforestation
· . ~
in Amazoma
Emilio F. Moran and Eduardo Brondizio
This chapter describes a project that linked traditional social science and
biological field methods with remotely sensed data to further understanding of
how human decisions about land use have influenced both rates of deforestation
and subsequent secondary successional rates of regrowth in Amazonia. The
impetus for this project was a workshop held in 1987 that introduced ecological
anthropologists to remotely sensed data as a tool in addressing substantive social
science questions at a regional scale. The workshop emphasized the importance
of developing a partnership between social scientists and colleagues having suf-
ficient expertise in remote sensing to solve the complex technical problems likely
to be faced, and it did so without failing to note that this partnership would be best
served if the social scientists developed a minimum level of proficiency in remote
sensing to facilitate joint research and analysis.
Much of the promise of the new remote sensing techniques comes from
expanding the areal extent of studies so that regional-scale phenomena such as
land-use change can be addressed. The very advantages of small-scale studies
(intimacy with informants, richness of the social network, insights into household
structure) limit the ability of investigators to examine larger-scale phenomena.
Remote sensing's larger spatial capabilities expand the kinds of questions that
can be studied.
The published work on Amazonia in the 1970s and early 1980s spoke of
devastating deforestation, desertification in the humid tropics, and wholesale
conversion of tropical forest to pasture; it also made incorrect assumptions, such
as 100 percent combustion of forest biomass (Lean and Warrilow, 1989; Booth,
1989~. These themes, commonly expressed in studies based on remotely sensed
94
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EMILIO F. MORAN AND EDUARDO BRONDIZIO
95
data, did not ring true to those who formulated the project documented in this
chapter. Past social science research in the area had noted farmers' complaints
about the difficulties they faced from rapid regrowth of the vegetation cover
following cutting and burning of forest (Moran, 1976, 1981~. Secondary succes-
sion rapidly covered exposed ground and resulted in substantial land cover. Yet
the large-scale work using remotely sensed data hardly mentioned secondary suc-
cessional vegetation and rarely if ever suggested the significance of this vegetation
to processes such as carbon sequestration, biodiversity, and land-cover dynamics.)
The result of these reflections was the decision to craft a set of proposals
based on the same technology as that used by the remote sensing community-
Landsat Thematic Mapper (TM) digital data to understand land-use/land-cover
change dynamics following deforestation, particularly the factors that might ex-
plain the differential rates of secondary succession. A grant provided by the
National Science Foundation's (NSF) Cultural Anthropology Program enabled
one of the authors (Moran) to become familiar with the theory and techniques of
remote sensing. In fact, the Senior Scholar's Methodological Training Grant
programs has provided support for several environmentally oriented anthropolo-
gists to acquire technical skills in other disciplines and has substantially increased
the number of scholars engaging in this type of work. The following chapter by
Entwistle et al. is an example of another means of linking remote sensing and
social science. Following this 1-year learning period, grant support from the NSF
Geography and Regional Science and Human Dimensions of Global Change
programs made it possible to apply these newly acquired skills to the questions
raised above.
The second author of this chapter (Brondizio), who had acquired some of
these skills at Brazil's National Institute for Space Research (INPE), followed a
reverse trajectory. He had familiarity with agronomy, vegetation ecology, land-use
studies, and remote sensing research and undertook to learn social science methods,
especially ethnographic skills, while pursuing a Ph.D. in environmental sciences.3
A common research question meaningful to the social and environmental
sciences what forms of land use lead to given rates of secondary successional
regrowth in Amazonia-provided the epistemological basis for our collabora-
tion. The choice of soil by a homesteader, the choice of area to be cleared, the
method used for clearing, the timing of burning, the choice and sequence of crops
planted, and the frequency of weeding all affect the rate at which pioneer species
can colonize an area of land, the composition of that succession, and the differen-
tial survival of mature forest species. The study of secondary succession requires
integration of conventional site-specific research methods in vegetation ecology and
ethnographic data with more inclusive scales through remote sensing analysis of
land-use and land-cover patterns (Moran et al., 1994; Brondizio et al., 1996~.
This chapter presents examples from our work that illustrate the linking of
remote sensing and human ecological questions in understanding land-use and
land-cover change in Amazonia. The chapter does not describe specific method
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LAND-USE CHANGE AFTER DEFORESTATIONINAMAZONIA
ology and technical details related to image processing, spectral analysis, and
vegetation/soil inventory techniques, for which readers are referred to published
papers written by the authors and their collaborators (Mausel et al., 1993; Moran
et al., 1994; Brondizio et al., 1994, 1996; Li et al., 1994~. As with other chapters
in this volume, the objective here is to discuss how collaboration between the
social scientist and the remote sensing expert developed, summarize the findings
and insights gained by linking remotely sensed data to traditional social science
field research, and explore ways of advancing this type of collaborative work in
the future (e.g., Mausel et al., 1993; Moran et al., 1994, 1996; Brondizio et al.,
1994, 1996; Li et al., 1994; Brondizio, 1996; Randolph et al., 1996; Tucker,
1996; Tucker et al., in press). The work discussed here brought together remote
sensing, botany, environmental sciences, soil sciences, anthropology, and geog-
raphy. It did so incrementally, as interest grew in the issues raised by our
research among collaborators in Brazil and the United States. In other words,
building on a core set of questions and the expertise of anthropology, ecology,
and remote sensing, the project has expanded to address other concerns that
flowed naturally from the original set of propositions. This expansion was antici-
pated and was integrated without difficulty. Even now, the project anticipates
incorporating climatologists, zoologists, demographers, economists, and conser-
vation biologists.
THE VALUE ADDED OF SOCIAL SCIENTISTS' INTEREST IN
REMOTE SENSING ANALYSIS
Social scientists bring to the analysis of global change and its remote obser-
vation a concern about and an expertise in the behavior of people at the commu-
nity and household levels and a desire to understand the human face behind the
pixels (see Geoghegan et al., in this volume). When looking at a satellite image,
for instance, social scientists are inclined to search for land-use patterns associ-
ated with distinctive socioeconomic and cultural differences. Consequently, they
search for driving forces behind land-use differences and for land-cover classes
that represent culturally and biologically meaningful differences, in contrast to,
say, naming classes after a standard vegetation class. For example, the timing of
credit availability for pasture or cocoa development can help determine when one
might begin to see the appearance of these classes with higher frequency on a
landscape, or the creation of a class called "rota," which is a mixed subsistence
garden dominated by maniac and bananas. This poses a challenge in that if a
culturally meaningful category is present (e.g., palm agroforestry) and is associ-
ated with important behavioral differences (i.e., particular steps in preparing
these orchards through time until they reach the desired density), then an effort
must be made to sample enough cases so that the phenomenon can be distin-
guished spectrally and classified (Brondizio et al., 1994~. This may not always be
possible, but it is a challenge that social scientists, or at least anthropologists, are
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EMILIO F. MORAN AND EDUARDO BRONDIZIO
97
likely to bring to the task of field work and land-cover classification. On the
other hand, while a culturally meaningful category may exist, it may be so rare
that one cannot possibly obtain enough observations to separate it spectrally, or it
may not be different from other culturally differentiated classes that are spectrally
alike. It is still a challenge, for example, to differentiate between many types of
agroforestry and other mixed-crop systems given the current resolution of satel-
lite images. Some of these problems may persist until such time as orbital
satellites with a resolution of 1 m are available to researchers. The first satellites
with this resolution are expected to be launched in the next year or two and,
because they are launched by commercial enterprises, are expected to make data
available more promptly than is the usual current practice and to customize the
data to the needs of users.
Without a social scientist as part of the team, culturally important dimen-
sions of land cover may quite possibly be overlooked by scientists who bring a
nonlocal or purely remote point of view to the analysis of the data. The best
example from our work is the discrimination of managed (palm agroforestry)
from unmanaged floodplain forest in the Amazon estuary (Brondizio et al., 1996~.
Whereas these are vegetation classes with extremely similar structural character-
istics and thus are commonly mapped together, managed floodplain forest has
local economic significance that requires attention when one is studying an estua-
rine population at a regional scale. By combining traditional ethnoecological
field techniques that elicited culturally meaningful categories (A~caisal) with spa-
tial distribution considerations elicited by the satellite data, it has been possible to
distinguish between these two culturally and economically distinct vegetations
(i.e., managed and unmanaged floodplain forest). When the importance valued of
the palm Euterpe oleracea reached 0.6, it became possible to distinguish spec-
trally a managed Scar palm agroforestry grove from the adjacent floodplain forest
from which it had been developed by local farmers (Brondizio et al., 1994:261~.
While there is no substitute for the use of traditional ethnoecological field
data collection to obtain a deep understanding of native knowledge of the envi-
ronment, it is possible in cases such as that described above to collect a sufficient
number of observations to create spectrally differentiable classes of land cover
from native categories although success in this enterprise will rarely economize
on data collection costs. What will be gained, as in most applications of remote
sensing, is the ability to map at a regional scale the distribution of a land-cover
class that is meaningful to a local community over a much larger landscape than
is otherwise possible (Brondizio et al., 1996~. The value of forecasting cereal
harvests and yields of major commodities has been accepted for years in agri-
business. There is no reason, other than the more modest resources of the scien-
tific community, why forecasting of harvests of locally valuable crops, such as
maniac, bananas, or agroforestry groves marked by a dominant, cannot be under-
taken. One of the important results of the study of palm agroforestry has been to
show in dramatic fashion the very large areal extent of this economic activity, its
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LAND-USE CHANGE AFTER DEFORESTATIONINAMAZONIA
economic value to the regional economy, and the achievement of this outcome
with minimal loss of forest (a five-fold rise in economic value at less than a 2
percent loss of forest cover).
Even with current limitations on spatial resolution that can mask households'
complex patterns of land use, anthropological and geographical understanding of
the spatial distribution of sizes and locations of agricultural fields makes it pos-
sible to infer and interpret land-cover patterns that are distinguishable spectrally.
This ability has been enhanced with the growing use and accuracy of technology
that permits accurate location, such as Global Positioning System (GPS) de-
vices.5 However, the difficulties of distinguishing among coffee plantations,
early secondary succession, and degraded pastures should not be taken lightly.
Few analysts have tried, and even fewer have succeeded, to differentiate spec-
trally among types of crops, types of pastures, and types of agroforestry. The
more homogenous a stand is, the more likely it is to be identified consistently
with a high degree of accuracy, whereas for mixed and heterogeneous vegetation
formations, such accuracy is difficult to obtain. Our own work has been able to
differentiate among three distinct structural classes of secondary succession with
an accuracy of 92 percent, and between managed and unmanaged floodplain
forest with an accuracy of 81 percent (Mausel et al., 1993; Brondizio et al., 1996~.
These successes do not suggest that achieving these results was easy. On the
contrary, many classes we wish to differentiate have remained elusive. Monitor-
ing oil palm and cocoa, for example, has to date proven impossible given the very
large spectral differences among their various developmental stages. We believe
it should be possible to do so if sufficient resources are devoted to collecting
enough observations for the distinct steps in the development of these plantations-
a goal very different from ours of understanding secondary successional processes.
An excellent example of the application of remote sensing to fundamental
issues in social science is a study by Behrens et al. (1994) that shows how
settlement history mediates the effect of population pressure on indigenous land
use. Sendentism and the market opportunities that promote it seem more impor-
tant drivers of land-use intensification and tropical deforestation among contem-
porary native Amazonians than population growth itself. Village formation and
cattle ranching are associated with greater landscape heterogeneity, but fewer
woody species. Concentrating in large villages a population that has been distrib-
uted areally over the landscape can intensify deforestation, particularly when
exacerbated by the development of pastures and irrigated rice cropping. The
study of intensification is of fundamental interest to the understanding of human
societies through time, and remote sensing is an excellent tool for observing the
extent and intensity of its impact.
One of the most important contributions social scientists can make to this
type of research is to help construct data collection protocols that capture the
types of socioeconomic data most closely related to land-use dynamics. It is all
too common for those outside the social sciences to try to explain land-cover
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EMILIO F. MORAN AND EDUARDO BRONDIZIO
99
change in terms of population growth, rather than applying the more nuanced
approach needed to understand the relationship between population (growth,
distribution, structure) and the environment. A current project of the authors
involves investigating, at the level of the farm property, the role played by the
demographic structure of each household in changing uses of the land, with a
view to predicting rates of deforestation from a knowledge of household compo-
sition. There is a need to develop a protocol for the minimal data needed to
support ecological and remote sensing analysis and also be meaningful to the
social sciences. For example, such a protocol might include data related to
production systems (types of economic importance), a calendar of activities
throughout the year (land clearing, planting, weeding, harvesting, fallowing), soil
and vegetation management techniques, the demographic composition of house-
holds and populations, time allocation in different production systems, land-
tenure structure, and an ethnoecological classification of ecosystem components
(Moran, 1993; Brondizio, 1996~. For example, one should expect significant
differences in the way land cover develops and changes through time as a product
of, say, private or communally held forest. Current studies by our research group
in seven Latin American countries are aimed at elucidating the impact of tenure
and other social organizational arrangements on the composition and longevity of
forest cover through time.
METHODS
Levels of Analysis and Site Selection
From the outset, we have followed a systematic approach to site selection
and comparison. Taking a contrary view to that commonly held, we hypoth-
esized that the differential rate of secondary succession would most likely be
influenced by initial soil fertility, the history of land use of a deforested area, and
the spatial pattern of land-use and land-cover classes. Soil fertility had seldom
been related to or soil data collected in studies of forest ecology and succession
(Buschbacher et al., 1988~. Since tracking of age classes of secondary succession
and biomass accumulation in such vegetation had not been performed in
Amazonia using Landsat TM (and had been unsuccessful using the Multispectral
Scanner [MSS]), we began our study by examining two locations. Each was
characterized as having relatively fertile soils, so that if it were technically pos-
sible to differentiate stages of secondary succession, the change might be measur-
able in the relatively brief time span between 1984 and 1991 during which TM
was available. For the sake of contrast in both environmental and land-use terms,
we selected an upland site that was well known to one of us from earlier work
(Moran, 1976, 1981) and an estuary area where we had done preliminary work
(Murrieta et al., 1989~. Following 2 years of research at these two sites, we
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100
LAND-USE CHANGE AFTER DEFORESTATIONINAMAZONIA
worked at three other Amazonian sites, characterized by relatively nutrient-poor
soil conditions and different patterns of land use.
One of our goals from the outset was to link detailed ethnographic data,
species- and stand-level data, and land-use histories to the spectral analysis so
that we could achieve not only a field-level understanding of changes in land
cover, but also a regional analysis of land-use and land-cover change. Doing so
required that we work with a large portion of the TM scene, and that our sampling
design be distributed over the image in order to incorporate spatially variable
phenomena, such as different kinds of settlements, different land-tenure arrange-
ments, different types of vegetation, and different soil types. Thus, we sought to
link the behavior of households in farms and settlements to regional-scale pro-
cesses of land-cover change, especially secondary succession. We also wished to
link these results to global carbon models and Amazon Basin models a task that
has been pursued more directly by Skole and his collaborators (e.g., Skole and
Tucker, 1993~.
Figure 5-1 illustrates the multiscale and multitemporal approach pursued in
our studies. Our analysis begins by selecting locations that fit our fertility gradi-
ent design and have representative patterns of land use and population distribu-
tion. We also take into account data availability and the presence of colleagues
with whom joint work might be undertaken that would enrich local institutions
with both data and expertise. For the selected locations, we seek available cloud-
free images of the study areas; depending on availability, we also try to obtain a
set of images providing data intervals within which the processes of change can
be observed, at least one of which is coincident with our field research. These
data are then georeferenced and registered, exploratory spectral analysis is car-
ried out in small subsets representative of different patterns of land cover, and
this analysis is then used to carry out unsupervised classification of land cover
over the entire study area. Details of our technical procedures have appeared in a
number of publications (Mausel et al.,1993; Moran et al., 1994; Brondizio et al.,
1994, 1996; Li et al., 1994).
We then proceed to the field, not merely to carry out field observations of
land cover the most common method of ground truthing but also to interview
at length land users who are identified from the initial analysis as having land-
cover classes of interest for sampling and are distributed over the entire image.
All of these visits to farms generate valuable information. Some are not entirely
successful, either because the land has undergone transformation since the TM
scene was taken or because there is error in the unsupervised classification.
Detailed household surveys, with particular emphasis on the history of land use,
are then undertaken. Following these surveys and a visit to the forest or second-
ary successional area, we request permission for the larger team to come to the
property to carry out a detailed soil and vegetation inventory.
The resulting data are entered into a spreadsheet in the field, and adjustments
are made in the sampling to ensure that a representative number of classes of
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EMILIO F. MORAN AND EDUARDO BRONDIZIO
Spabal scale
~ 4
Landscane/Recional level
Vegetation class
level |
Farm/household
level
Soil level
c.brondizio, 94
00
IGPS (IDs) 1
~ , ~
(agrofQrestryN ~ `, I>
fat (agriculture
- Shallow)
ok,.
101
GeoreferenceJ/Registered Images
MuNitemporal images- Landsat/Spo
Spectral modeling/classification
Multitemporal Land Use analysis
is ~
Scaling up ~ down C
(Imagec->Ground based data)
\7 ~
A
V V
Multilevel classification system
Structural ~ Floristic inventory
Vegetation profile
Agroforestry/Crop inventory
Cycles/ManagemenVProductior
. 1 . 1
Farm/Household
Composition/economy
Land tenure
Land use system
Land use history
Ethnoecology
Technology/management
| Crop!forest yield-
1 1
Soil profile - O to 1 meter
PhysicallChemical camp.
Soil color
FIGURE 5-1 Methods of multilevel analysis of land-use and land-cover change.
vegetation are sampled. Each area at which soils and vegetation are sampled is
georeferenced with a GPS device, every effort being made to choose study areas
large enough so we can be sure of their location on the printouts of the TM scene
we have prepared in advance and laminated for field use. These image printouts
are generally prepared at a scale of 1:30,000 with a 1 x 1 km grid of Universal
Transverse Mercator coordinates that allows us to locate each site (through use of
GPS) while in the field. Fields as small as 1 hectare (ha) are clearly visible in
these image maps, although we commonly select larger areas within which to
take vegetation and soil samples. The use of these image printouts prepared at a
fine scale and enhanced for visual interpretation has proven particularly valuable
in extracting field information. The relative ease of understanding color compos-
ites (TM bands 5, 4, 3~6 makes it possible to discuss land-use and land-cover
features with local farmers with a minimum amount of explanation. Their discus-
sion of the image provides an invaluable source of information about land-use
and land-cover dynamics and makes sense out of the distribution of the different
types of land cover encountered. In addition to the field-sampled plots, one or
more members of the team collect "training samples" (i.e., visual observation of
hundreds of locations) in order to obtain a robust supervised classification of
land-cover classes upon returning from the field.7
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LAND-USE CHANGE AFTER DEFORESTATIONINAMAZONIA
Upon returning from the field, we use the GPS-referenced field observations
to develop the supervised classification. We also perform accuracy analysis to
determine the extent to which classification accuracy of at least 85 percent have
been achieved. A second season of field work has characterized our work so far,
at which time we are commonly able to double our field inventory data and refine
the accuracy of the land-cover classes.
During the past 5 years our group has developed an extensive data set. This
data set is focused on secondary succession and land-use and land-cover change
in five Amazonian regions distributed along a soil fertility gradient representing
relatively nutrient-rich (eutrophic) to relatively nutrient-poor (oligotrophic) con-
ditions.
Study Areas
Altamira, in the Xingu Basin, is characterized by patches of nutrient-rich
soils (alfisols) and less fertile soils (ultisols). Ponta de Pedras, in Marajo Island,
is characterized as a transitional environment composed of upland nutrient-poor
oxisols and flood plain alluvial soils. Igarape-A~cu, in the Bragantina region, is a
mosaic of oxisols and ultisols. The soils of Tome-A~cu are dominated by oxisols
and ultisols, both acidic and low in nutrients. They are less sandy in textural
characteristics than those in Bragantina (Igarape-A~cu) but more so than those in
Altamira or Marajo. Finally, Yapu, located on the Vaupes (a tributary of the Rio
Negro), is composed of large patches of extremely nutrient-poor spodosols inter-
mixed with stretches of oxisols.
Land use varies among these areas. Altamira, which lies along the Transamazon
highway, began being colonized in 1971 and has experienced high rates of defor-
estation and secondary succession associated with the implementation of
agropastoral projects. In contrast, Marajo has historically been home to native
nonindigenous (i.e., Caboclo) populations occupied primarily in agroforestry ac-
tivities in the floodplain and swidden agriculture in the uplands, along with some
recent creation of pastures and mechanized agricultural fields. Land use in the
Bragantina region has gone through several phases; today short-fallow swidden
cultivation is dominant, given the proximity of the Belem market for producers.
Cultivation of secondary growth areas has been common for decades, and islands
of mature forest are rare. Tome-A~cu has experienced the most intensive agricul-
ture of the five sites (a black pepper monoculture until the late 1960s), and for the
past two decades has been associated with agroforestry development carried out
by the Japanese colonists who have lived there since the 1930s. It is now experi-
encing the start of pasture formation. Finally, the Colombian Vaupes site at
Yapu, populated by indigenous Amazonians, is characterized by more traditional
long-fallow swidden cultivation based on bitter manioc.
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EMILIO F. MORAN AND EDUARDO BRONDIZIO
~ '
~> Or - , ~(I ~
~\t ~42~
C
)~ ~
i~
\
Altamira, Xingu Basin, Para, Brazil
. Ponta de Pedras, Marajo Island, Para, Brazil
3 Igarape-Agu, Bragantina, Para, Brazil
4 Tome-Agu, Para, Brazil
5 Yapu, Vaupes Basin, Colombia
103
FIGURE 5-2 Research sites in Amazonia of the Anthropological Center for Training
and Research on Global Environmental Change (ACT), Indiana University.
Distribution of Research Locations
Figure 5-2 shows the locations of the five study areas discussed above, and
Plate 5-1 (after page 150) shows Landsat images of each location. In each region,
areas representative of the major vegetation types, including different forest types
and fallows, are selected for sampling. Altamira is represented by 20 sites (18
fallows and two forests), Marajo by 14 sites (10 fallows and 4 forests), Bragantina
by 19 sites (16 fallows and 3 forests), Tome-A~cu by 13 sites (12 fallows and 1
forest), and Yapu by 8 sites (5 fallows and 3 forests), for a total of 74 sites. The
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104
LAND-USE CHANGE AFTER DEFORESTATIONINAMAZONIA
detailed soil and vegetation inventories permit careful characterization of each loca-
tion.
Vegetation and Soil Inventory and Processing
Our strategy is comparable across the 74 sites. The majority of plots and
subplots are identical in size and shape, allowing cross-comparison and integra-
tion at the level of plot, site, and location. In most cases (except mature forest),
the area sampled per site is 1,500 m2. Plots and subplots are randomly distrib-
uted, but nested inside each other to account for the detailed inventory of trees
(diameter at breast height [DBH] greater than or equal to 10 cm), saplings (DBH
2-10 cm), seedlings (DBH less than 2 cm), and herbaceous vegetation. In the
plots, all the individual trees are identified and measured for DBH, stem height
(height of the first major branch), and total height. In the subplots, all individuals
(saplings, seedlings, and herbaceous vegetation) are identified and counted, and
diameter and total height are recorded for all individuals with DBH equal to or
greater than 2 cm.
Species identification is carried out by experienced botanists in the field and
checked at the herbarium in Belem, Para. Botanical samples are collected from
half of all species identified to ensure accuracy of taxonomic identification. At
each site, soil samples are collected at 20-cm intervals to a depth of 1 m. Soil
samples are analyzed at the soil laboratories in Belem for both chemical and
physical properties. A stand inventory table, including absolute and relative
frequency, density, dominance, basal area, importance value, and stem and total
height, is prepared for each of the inventoried sites.
A soil fertility index summarizing differences among regions is used (Alvim,
1974~. It is important to note that our comparisons take into account only upland
soils of the Marajo site. The more fertile floodplain is not included in this
analysis since it is not comparable at this level with the data of the other four
locations, all of which have upland soils with very different characteristics from
those of floodplains. The index aggregates pH, organic matter, phosphorus,
potassium, calcium and magnesium, and aluminum (inverse value). It was pre-
pared for each depth (0-20, 20-40, 40-60, and 80-100 cm), and an average index
was prepared across depths.
PATTERNS OF SECONDARY SUCCESSION IN AMAZONIA
The data obtained using the methods described above have yielded a number
of findings with regard to soil physical and chemical patterns in the study regions,
variations in rates of regrowth, and stages of regrowth in Amazonia.
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LAND-USE CHANGE AFTER DEFORESTATIONINAMAZONIA
of a height equal to or less than 2 m during the first 2 years of fallow. In terms of
basal area, this stage presents a variation that ranges from 1 to 10 m2/ha. The
majority of individuals have DBH of 2 to 5 cm. In age terms this period encom-
passes around the first 5 years of fallow, but it may be much longer in areas
subjected to heavier land-use impacts.
SS2 can be characterized as a period of thinning of herbaceous and grass
species and a rapid increase in sapling dominance, with small trees beginning to
appear. While saplings account for most of the total basal area and biomass,
young trees dominate the canopy structure. Canopy and understory become
increasingly differentiated, but stratification is still subtle. The increase in shade
during this stage is an important element in species selection. Average height is
7 to 15 m, and DBH is 5 to 15 cm. Basal area ranges from 10 to 25 m2/ha. This
period encompasses the next 10 years fallow, that is, 6 to 15 years after abandon
ment.
SS3 is marked by a growing stratification between understory and canopy
and by the declining contribution of saplings to total basal area and biomass.
Average height is 13 to 17 m; however, a considerable number of shorter (6 to 13
m) and very tall (20-30 m) emergents occur. Individuals with DBH of 10 to 15
cm are still of major importance at this stage, but a considerable number of larger
individuals are present. In terms of basal area, this stage is similar to the interme-
diate stage. One of the reasons for this relates to the process of species selection
that occurs between SS2 and SS3. Fast-growth trees of SS2 (e.g., Cecropia spp.)
that contribute a major portion of the total basal area at this stage tend to give way
to forest tree species during SS3. Therefore, instead of a progressive increment
in basal area from SS2 to SS3, there is replacement of the species and individuals
contributing to basal area. In age terms, this is a stage that encompasses fallows of
more than 15 years. However, we found thatin Altamira, this type of structure could
be achieved in about 11 years.
Mature forest vegetation varies widely within the Amazon. Average height
varies from less than 15 m to around 24 m. However, one can distinguish
between forest and advanced regrowth by taking into account additional features
that characterize mature forest vegetation. First, species composition needs to be
considered as a unique discriminator of a mature forest environment. Mature
forests have higher species diversity. The presence of very tall emergent trees
with large diameters is also a distinctive feature. Most emergent trees have DBH
above 30 cm and height greater than 15 m. Basal area in mature upland forest
ranges from 25 to 50 m2, thus providing a distinct structural difference from
advanced regrowth (10 to 25 m2/ha) that facilitates spectral separation.
This model of regrowth stages can be applied to the Amazon region if land-
use intensity, landscape diversity, and soil fertility variables are taken into ac-
count at the regional and local levels. The proposed regrowth classes provide a
baseline for remote sensing analysis and large-scale studies of land-use and de-
forestation dynamics. Structural characteristics of the vegetation such as those
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EMILIO F. MORAN AND EDUARDO BRONDIZIO
111
described above influence spectral information. Differences in average stand
height can be correlated with increased absorption (i.e., lower reflectance) of the
visible bands (i.e., 1, 2, 3 in Landsat TM). Likewise, these structural features
lead to differing reflectance values among SST, SS2, and SS3 and mature forest
in the near- and mid-infrared bands (i.e., 4, 5, and 7~. Understanding these
structural/spectral relationships gives social scientists a powerful tool for study-
ing land use and agricultural cycles of human populations. The structural param-
eters presented above allow one to discriminate with modest effort between areas
recently and long abandoned, and to collect good-quality training samples for
image-supervised classification (see Mausel et al., 1993, for a fuller discussion of
spectral characteristics of vegetation types).
If one keeps in mind the general features of land-cover classes and which
features have the greatest role in spectral differentiation, field research observa-
tions can generate information of considerable value to the classification of land
cover. Good-quality pastures have higher visible-band reflectance than degraded
pastures because of their more homogeneous surface and minimal shadow and
the presence of soil as a component of reflectance (also producing a relatively
high mid-infrared reflectance). A degraded pasture and initial secondary vegeta-
tion will have a lower visible-band response due to greater vegetation, less soil,
and increased chlorophyll absorption. We take the categories of degraded pas-
ture and SS 1 to be equivalent: a degraded pasture is a cultural category meaning-
ful to a rancher who sees a pasture that has been invaded by woody growth and is
no longer capable of sustaining cattle; SS 1 is an ecologist's category, used when
a rich diversity of pioneer species occupies land that was previously cultivated or
deforested and that had been characterized by a dominance of herbaceous and
woody species. In degraded pasture or SST, the near-infrared will have higher
reflectance due to mesophyll reflectance, but the mid-infrared will have lower
reflectance than in pasture as a result of greater absorption of water by the
vegetation.
The developing canopy in SS2 has higher biomass and moisture than are
found in SS 1. Thus, while the visible bands will not differentiate it from SS 1, the
green-to-red ratio will be higher. The mid-infrared is lower than in SS1 because
of increased shadow, a pattern that continues with the greater growth of the
vegetation. In the advanced stages of regrowth, near-infrared and mid-infrared
reflectance continues to drop because of increased shadowing and increased mois-
ture levels in the vegetation.
Thus it is important for field work to distinguish the pattern of canopy and
understory, the amount of exposed soil, the surface roughness of the vegetation,
and the amount of shadowing to assist in spectral analysis of these patterns in the
satellite data.
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LAND-USE CHANGE AFTER DEFORESTATIONINAMAZONIA
IMPLICATIONS FOR TRAINING AND RESEARCH
One of the lessons that emerges from our experience is the importance of
taking time to learn the basic theoretical and methodological approaches of col-
laborators in other disciplines. While it may be possible to develop a clear divi-
sion of labor between collaborators, the collaboration will proceed more smoothly
if social scientists are familiar with the theoretical principles behind spectral
information and are aware of the limitations of sensors and the sampling require-
ments for achieving acceptable levels of accuracy in classification. Likewise,
knowing how to obtain reliable data from interviews in order to learn firsthand
about the variability in human behavior and culture, as well as in plant distribu-
tions and stand structure, makes the remote sensing specialist more realistic about
what can be brought back from the field and the reasons for trying to represent
classes of local economic or environmental interest. It is common among remote
sensing practitioners to perform field work largely to check the accuracy of
categories that emerge from spectral analysis or to give them names, without
much interest in changing classes that may prove to correspond poorly to field
reality. The feedback from social science research and the incorporation of
culturally meaningful categories are important contributions to the mapping of
changes between land-cover classes and understanding of the driving forces of
such changes. One of the important reasons for social scientists to play an
increasing role in efforts to classify land uses with remotely sensed data is that in
so doing they can begin to shift the applications of remote sensing from a map-
ping mode to one that seeks to explain social structures and processes land-use
dynamics, the lag time between commodity price shifts and landscape transfor-
mations, the estimation of yield from noncereal and even agroforestry crops, and
the internal structure of households as revealed by the behavioral outcomes of
that structure that are visible in land-cover changes.
Thus whenever possible, graduate students should be encouraged to pursue a
global change graduate minor that will give them at least minimal combined
exposure to the theories and methods of the social sciences, the biological sci-
ences, and remote sensing. Lacking this, private foundations and federal agen-
cies should be receptive to institutions wishing to support intensive training
programs designed to introduce faculty and graduate students to these skills so
they can participate effectively as members of multidisciplinary teams on global
chance. Indiana University, with support from the Tinker Foundation, has pro
vided such training for the past 2 years to Brazilian and Mexican colleagues.
Over the next several years, the NSF-funded Center for the Study of Institutions,
Population and Environmental Change at Indiana University will conduct sum-
mer institutes addressing these needs, as well as continue to offer visitors month-
long individualized training linking social science and remote sensing to ques-
tions of environmental monitoring and global change, particularly in forested
environments.
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EMILIO F. MORAN AND EDUARDO BRONDIZIO
113
It may be hoped that other institutions will seek additional ways of meeting
the challenge of addressing what sometimes becomes a major obstacle to collabo-
ration between social scientists and those in the remote sensing community: a
lack of familiarity with the techniques of the other field and with how their
distinct but complementary skills can be linked to address questions of joint
interest. The role of NSF in supporting multidisciplinary work through its Pro-
gram on the Human Dimensions of Global Change has been crucial to advances
made in this area to date. Continued support for such work by NSF and other
agencies, such as the National Institute of Child Health and Human Development
(NICHD), the National Aeronautics and Space Administration (NASA), and the
National Oceanic and Atmospheric Administration (NOAA), would help in fur-
ther eliminating existing barriers and creating a fertile ground for additional
contributions to a basic understanding of human impacts on a changing environ
ment.
In addition to a lack of familiarity with one another's skills, another notable
obstacle to collaboration between social scientists and experts in remote sensing
is the continuing high price tag for obtaining high-resolution satellite data, such
as data from TM and the French Systeme Pour ['Observation de la Terre (SPOT).
Despite regular promises to the community that the price will soon return to "the
price of acquisition," many obstacles remain. While archived data have come
down in price, more recent TM scenes still cost over $2,000, and the recent TM
scenes have had serious problems involving uncertainties over sensor calibration
that make such an expense increasingly risky. A spatial resolution of 30 m or
better is necessary to address many of the human-dimension questions of concern
to the social sciences, yet the discussion of Earth Observing System (EOS) instru-
mentation has seldom included input from social scientists to ensure that con-
cerns related to the human dimensions of global change would be given high
priority in sensor design. The remote sensing community needs to develop a
mechanism for liaison with the social science community that engages them both
in the decision-making process regarding the kinds of earth-observing instru-
ments needed to understand human impacts on the environment at a variety of
scales. Coarse scales tend to mask both environmental and human variability-
one of the main things threatened by environmental change. To understand
human and biological diversity, we need instrumentation that is sensitive to these
fine-scale patterns and permits the linkage of fine-scale field research to remotely
sensed data (see Cowen and Jensen in this volume, who raise similar issues
relevant to work in urban areas). The other notable constraint is cloud cover and
shadow, the latter especially in mountainous areas. Advances in radar technol-
ogy should help with this problem since clouds do not interfere with radar.
However, applications of radar data to land-cover analysis will require consider-
able technical advances before they can be used profitably by the social sciences.
A new generation of sensors is expected in the near future (e.g., Earlybird,
Quickbird, the ASTER thermal emissions radiometer, Moderate Resolution Im
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4
LAND-USE CHANGE AFTER DEFORESTATIONINAMAZONIA
aging Spectroradiometer [MODIS]). It is likely that social scientists can help
evaluate the capabilities of proposed sensors, as well as contribute to the discus-
sion of data availability. As important as it is to improve spatial resolution, there
needs to be a commitment by remote sensing institutions responsible for data
recording that information will be stored for regions all over the world. Images
from SPOT sensors, for instance, despite their higher spatial resolution, have
restricted availability for isolated areas. This results in a spotty land-cover change
record that renders multitemporal analysis limited in scope.
In terms of spectral resolution, there is a need to discuss the possibility of
dividing bands such as TM4 and TM5 into smaller spectral regions, and to evalu-
ate whether and how such a change could improve future studies of land-use and
land-cover changes, especially those related to agriculture. By the same token, it
is still unknown what kind of information would be available if a thermal band
(such as TM6) were designed at a higher spatial resolution, such as 30 or 20 m.
Landsat 7, which will be launched in 1998, is expected to have a 30-m thermal
band.
One point that is of particular importance to remote sensing analysis but is
frequently dismissed is the need to work with digital data that have been cali-
brated (i.e., converted to reflectance values). The implementation of technical
procedures for performing such calibrations requires considerable expertise found
only rarely outside of major remote sensing facilities. There would appear to be
enough capability within remote sensing institutions responsible for data recep-
tion to develop relatively automated procedures that would facilitate this kind of
data preparation for those, like social and biological scientists, who lack this level
of laboratory or technical expertise. Even some in the remote sensing community
use other procedures to get around the complex uncertainties involved in trans-
forming digital numbers to reflectance values.
In terms of software development, there should be continuous support for the
development of low-cost, interactive, yet powerful packages, such as IDRISI
(developed by Clark University) and MULTISPEC (developed by Purdue Uni-
versity). Such packages should include a range of statistical tools allowing
analysis of data for training samples to be used in supervised classification and
determination of the accuracy of thematic maps. Such software packages provide
an ideal tool for training social scientists, since they allow more effort to be
dedicated to image analysis and interpretation than to the learning process for the
software itself. Many social scientists are reluctant to work with digital data
because of the slow learning curve for many remote sensing and geographic
information system (GIS) packages, which translates into virtual inaccessibility
of image processing to its numerous potential users. The growing capability of
personal computer (PC) processors now frees new members of the community
from the need to rely on the UNIX platform for working with these data. Even
the powerful ERDAS Imagine image processing software is now available for the
PC at a cost that is affordable under the most modest of grants.
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EMILIO F. MORAN AND EDUARDO BRONDIZIO
CONCLUSIONS
115
Looking back over the past 5 years of our project linking detailed field
studies to remotely sensed data, we believe that on the whole, this joint work has
advanced our knowledge of important processes of land-cover change in
Amazonia and our knowledge of how to link data across levels of analysis, and
holds promise for further contributions in the immediate future. Without our
detailed field inventories and land-use histories, we would not have been able to
distinguish among three distinct stages of secondary succession in the TM images
(Mausel et al., 1993; Moran et al., 1994), to distinguish floodplain forest from
Scat palm managed floodplain forest (Brondizio et al., 1994, 1996), to demon-
strate the linkage between soil fertility and rates of secondary succession at a
more than highly localized scale (Moran et al., 1996), and to determine the
relative impacts of various land-use trajectories in specific soils on species com-
position and biomass accumulation (Moran et al., 1996; Tucker et al., in press).
The use of satellite remote sensing modified our approach to sampling so that it
became more widely distributed over the landscape than it would otherwise have
been. This brought us into contact with households, soils, and landscape patterns
different from those we would have sampled if we had relied on traditional
techniques. In turn, these contacts enhanced the regional scope of our conclu-
sions about land use and its impact on land cover and produced statistics for areas
much larger than would otherwise have been possible, while our detailed field
studies allowed us to modify land-cover classes and provided enhanced discrimi-
nation. Cumulatively, our various studies have developed structural criteria that
facilitate the application of these considerations in spectral analysis of satellite
data for other Amazonian regions.
The linking of remotely sensed data to traditional field methods in the social
and biological sciences has permitted more thoughtful sampling over a larger
region, addressing questions of decadal change that could not be examined
through traditional methods alone. We have documented land-cover change at
five separate Amazonian locations in 5 years a task that 10 years ago we would
have thought impossible even to consider. This change has been tracked with
detailed quantitative measures and accuracies of 85 to 94 percent that provide
levels of confidence rarely achieved by the traditional methods of the social
sciences.
Our work in Marajo, for instance, has changed how we conceptualize
Caboclo populations and their engagement in the regional economy. Study of the
intensification of flood plain agroforestry through a combination of household
interviews, field inventory, and image classification of these areas has shown that
the are al extent of agroforestry stands represents the most important production
system in the region. On the one hand, it changes the characterization of the
population from extractivists to intensive farmers forest farmers. Furthermore,
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LAND-USE CHANGE AFTER DEFORESTATIONINAMAZONIA
it shows that the level of food production can be increased without increasing
rates of deforestation (Brondizio, 1996; Brondizio and Siqueira, 1997~.
One can also examine changes in the densely populated Bragantina region.
This area, characterized for 100 years as an area dominated by smallholders,
commonly on 25-ha farms, has been undergoing transformation in recent years.
What is the extent of this transformation? Is it taking place only near towns or
along major roads, or is it pervasive? The use of remote sensing can help
monitor these changes in land cover and help us question their desirability in
social and environmental terms. At a time when cities such as Belem more
than ever need a green belt to supply them with produce, the traditional
sector that has supplied it may be disappearing as a result of uninformed
policies or lack of support.
Altamira likewise is a landscape that poses many questions for social and
environmental scientists. Will it begin to experience the same kind of logging-
related fires that have plagued the Paragominas region and Borneo? Our recent
work on the structure of households in the area using a property-level grid over-
laid with satellite image data suggests a rapid expansion of logging beginning in
1985 and accelerating in 1988. Whereas logging was concentrated within the
first 25 km from the town, in 1985, by 1991 loggers were altering areas more than
75 km from the town and desirable species have become increasingly rare closer
to town. What is the spatial distribution of pasture and other economically
significant land uses? What is the impact of road distance and road quality on
economic activity? In some preliminary work with the near- and mid-infrared
bands of TM, we detected a distinct pattern of land use in which the higher-
ground farms experienced greater deforestation and land use than those occupy-
ing lower positions in the landscape. Is this a product of soil type differences
along a soil catena, or of moisture saturation in lower sites? Can analyses using
infrared bands provide a quick way of identifying better soils for agriculture in
newly settled areas?
In the Japanese colony of Tome-A~cu there is evidence of incipient change
toward the expansion of cattle ranching and away from the intensive systems of
production that have characterized the past 65 years of occupation. The com-
bined tools of socioeconomic analysis and remote sensing can provide a means of
effectively monitoring such changes in culture and society that are of consider-
able theoretical and practical significance in terms of regional development.
Tome-A~cu has long been seen as offering an example of an alternative to cattle
ranching in Amazonia. Understanding of how and why this human community is
shifting to cattle has considerable economic and environmental significance.
Perhaps more important to discussions of global environmental change are
findings that show the large extent of carbon sequestration by secondary vegeta-
tion and its very high rate in the initial 10 years after abandonment, its spatial
variability as a function of soil fertility, and the role of land use in this process
(Randolph et al., 1996~. In documenting the role of soils we have also found
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EMILIO F. MORAN AND EDUARDO BRONDIZIO
117
some counterintuitive results, such as very large carbon pools in the soil and in
both surface and deep roots. Contrary to past wisdom suggesting that the root
systems of tropical moist forests are shallow, we see the legacy of roots deep in
the soil profile (Nepstad et al., 1994~.
The results of the studies discussed here have led us in some new research
directions, including examination of the role of the developmental cycle of do-
mestic groups in shaping the trajectory of land use and deforestation in frontier
regions and the role of community-level organizations in managing forest re-
sources. Household composition may explain differential rates of deforestation
through time better than current models focusing on migrant origins and flows.
The impact of age and gender composition on strategies of land use is being
examined with support from NICHD. This research will elucidate the impact of
aggregate migration flows relative to that of household types within the migrant
pool and the changing behavior of households through time as they mature and
change in composition. A second line of investigation, under the NSF-funded
Center for the Study of Institutions, Population and Global Change at Indiana
University, is incorporating the community level of organization into our stud-
ies a level that falls between the household and landscape levels on which we
have focused in the past. At this level, we will be examining the organization of
user groups within communities and the observable differences among groups
using forest resources within different property regimes and demographic spatial
distributions. This level will be linked to the field-level and landscape-level data
discussed in this chapter.
In both of the above new efforts, remotely sensed data play a key role at
every stage of the research from the exploration of types of land cover, to the
sampling approach taken in the field work, to the interviews with land users, to
the analysis of land-use changes in time and space. These plans suggest a pro-
ductive collaboration among social scientists, biological scientists, and the re-
mote sensing community for years to come.
ACKNOWLEDGMENTS
The authors thank the National Science Foundation, which through grants
91-00526 and 93-10049 has provided support for these five Amazonian land-use
studies. Support for carbon modeling has been provided by the Midwestern
Regional Center of the National Institute for Global Environmental Change.
Support to the second author from the Indiana Center for Global Change and
World Peace and NASA's Global Change Fellowship Program (3708-GC94-
0096) is gratefully appreciated. The work reported on here is the product of the
efforts of many on our team. From the United States are J. C. Randolph, Amy
Gras, Alissa Packer, JoAnne Michael, Joanna Tucker, M. Clara da Silva-Forsberg,
Fabio de Castro, Stephen McCracken, Warren Wilson, Cindy Sorrensen,
Bernadette Slusher, Vonnie Peischl, and Masaki Yamada. From Brazil are Mario
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LAND-USE CHANGE AFTER DEFORESTATIONINAMAZONIA
Dantas, Italo Claudio Falesi, Adilson Serrao, Lucival Rodrigues Marinho, fair da
Costa Freitas, Therezinha Bastos, and many others in collaborating institutions-
especially Empresa Brasileira de Pesquisa Agropecuaria/Centro de Pesquisa
Agroflorestal do Tropico Unido (EMBRAPA/CPATU). We extend our apprecia-
tion also to Ronald Rindfuss, who provided valuable comments, and to Paul
Mausel and the Department of Geography and Geology at Indiana State Univer-
sity, especially the staff of their Remote Sensing and GIS Laboratory, for wise
counsel at many stages of our work. We also wish to thank the many local people
who answered our questions about their uses of land with such good humor and
insight. The views expressed herein are the sole responsibility of the authors and
may not reflect the views of our funding sources or our collaborators.
NOTES
1 A notable exception is efforts by Woodwell et al. (1986, 1987) to differentiate between
mature tropical forest and secondary succession. That early effort, unfortunately, encountered the
limitations posed by the spatial resolution of the Landsat Multispectral Scanner.
2 Among those who have pursued the acquisition of these skills are Clifford Behrens, Bruce
Winterhalder, the late Robert McC. Netting, Endre Nyerges, and Emilio Moran. There may be
others that have escaped our notice. Others have preferred conducting cooperative work with remote
sensing specialists (e.g., Conrad Kottak) or taking advantage of courses offered at their universities
(e.g., George Morren and Thomas Rudel). The choice of whether to seek such training oneself or
rely entirely on the expertise of collaborators is an important one that reflects personal style, role on
the team, and synthesis goals. This is a mechanism that, if used by other social science disciplines,
could substantially increase the number of scholars engaged in this type of work although there are
other modalities for achieving this goal that are discussed later in the chapter.
3 Our work would not have been possible without collaboration with the remote sensing group
at Indiana State University's Remote Sensing and Geographic Information Systems Laboratory.
Collaboration with Paul Mausel has been particularly valuable over several years.
4 The importance value is a measure of relative dominance, frequency, and density.
5 GPS devices provide accurate location through triangulation using at least 3, and often more,
of the 24 satellites in the system.
6 Color composites made up of Landsat TM bands 5 (mid-infrared), 4 (near-infrared), and 3
(visible) provide a very realistic picture of the landscape that facilitates their field use in interviews.
7 During field work we use a "synthesis table" containing the structural characteristics of a
variety of land-cover classes to guide our training sample data collection. This synthesis table in-
cludes information such as average stand height and range of diameter at breast height for particular
land-cover classes to facilitate discrimination of classes of interest. This guide is based on earlier field
vegetation inventories carried out in the region, but it can also be based on existing studies carried out
by others.
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EMILIO F. MORAN AND EDUARDO BRONDIZIO
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Representative terms from entire chapter:
social scientists
